FunMuseSens
Sensing fungi in museums
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The project
FunMuseSens is developing a smart sensor system that detects fungal growth in museum environments long before visible damage occurs.
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Meet the team
The interdisciplinary team behind FunMuseSens combines expertise in heritage science, microbiology, sensor technology, and artificial intelligence.
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Master’s thesis
Sniffing out fungi: Design and build a VOC detector for museums.
About FunMuseSens
The challenge
Fungi pose a growing threat to museum collections, especially as climate change increases humidity and challenges traditional climate control. FunMuseSens develops a novel, ultra-sensitive sensor system that detects fungal growth at its earliest stages - long before visible damage occurs.
The technology
By combining low-cost chemical sensors with artificial intelligence, the system identifies characteristic airborne compounds released by fungi in real time. Unlike conventional monitoring methods, it operates continuously, requires no laboratory analysis, and can be deployed directly in exhibition spaces, storage rooms, and historic buildings.
Why it matters
This approach enables preventive conservation: curators and conservators receive early warnings, can act before infestations spread, and can confidently adopt more energy-efficient climate strategies. The technology is compact, affordable, and designed to function independently, making it accessible not only to large institutions but also to smaller museums and archives.
The project
FunMuseSens is an interdisciplinary project by the Academy of Fine Arts Vienna, the University of Vienna, and ICOM Austria, funded by the Austrian Academy of Sciences (ÖAW) through the Austrian Heritage Science 2.0 programme.
Fungal infestation on books in a museum archive
Team
Katja Sterflinger
PI
Microbial biodeterioration
EmailCopy email to clipboardInstitute of Natural Sciences and Technology in the Arts
Elke Kellner
Co-PI
Risk management
Stakeholder coordination
ICOM Österreich
Jürgen Zanghellini
Co-PI
Computational systems biology
EmailCopy email to clipboardBiochemical Network Analysis
Matthias Uiberacker
PostDoc
AI-driven data modelling
EmailCopy email to clipboardBiochemical Network Analysis
Johannes Tichy
Scientist
Museum bioanalytics
EmailCopy email to clipboardInstitute of Natural Sciences and Technology in the Arts
Johannes Zott
Master student
Sensor Technology and Calibration
EmailCopy email to clipboardBiochemical Network Analysis
Master’s thesis
Supervisor: Jürgen Zanghellini*| Department of Analytical Chemistry, University of Vienna September 23, 2026, Vienna
As part of the project FunMuseSens: Sensing fungi in museums, this master’s thesis focuses on develop-ing a complete device for detecting volatile organic compounds (VOCs) of biological origin. The sensor has already been tested in mock-up studies. Building on this work, the next step is to design and assemble a detection device for deployment in lab-studies and for future field trials.
Your task will be to design and manufacture the sensor housing using through 3D printing with high-performance polymers and integrate the sensor and control electronics into a complete system. The housing design will aim to minimise the amount of material required for printing. Sensor control will be implemented using an Arduino microcontroller programmed in C++.
The completed device will be evaluated for its practical suitability for lab and field deployment. Gas chromatography–mass spectrometry (GC–MS) will be used to identify VOCs released by the 3D-printed ma-terials in its final state and assessed whether the background signals could overlap with or mask biologically produced VOCs.
This project includes the following aims:
- Design and manufacture a housing using 3D printing (FDM) with high-performance polymers, includ-ing the focus on reducing material consumption.
- Integrate the existing sensor and implement Arduino-based control using C++ to assemble a complete detection device for lab and field trials.
- Evaluate the device’s practical suitability and use HS-SPME-GC–MS to investigate potential interfer-ence from VOCs released by the printed materials.
The duration of this project is set to 9 months.
- Great working atmosphere in a young and dynamic group working in computational biology.
- Access to a state-of-the-art computational environment.
- Co-supervision by a postdoctoral researcher.
Interested in this thesis project? For further information and the next steps, please contact juergen.zanghellini(at)univie.ac.at.